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Grasp Anything: Combining Teacher-Augmented Policy Gradient Learning with Instance Segmentation to Grasp Arbitrary Objects
After training a teacher policy to master the motor control based on object pose information, TAPG facilitates guided, yet adaptive, learning of a sensorimotor policy, based on object segmentation.
Learning Generalizable Tool Use with Non-rigid Grasp-pose Registration
Tool use, a hallmark feature of human intelligence, remains a challenging problem in robotics due the complex contacts and high-dimensional action space.
Accelerating Interactive Human-like Manipulation Learning with GPU-based Simulation and High-quality Demonstrations
Dexterous manipulation with anthropomorphic robot hands remains a challenging problem in robotics because of the high-dimensional state and action spaces and complex contacts.
Efficient Representations of Object Geometry for Reinforcement Learning of Interactive Grasping Policies
Grasping objects of different shapes and sizes - a foundational, effortless skill for humans - remains a challenging task in robotics.
Fourier-based Video Prediction through Relational Object Motion
The ability to predict future outcomes conditioned on observed video frames is crucial for intelligent decision-making in autonomous systems.
